【Hadoop综合实践】手机卖场大数据综合项目分析

本文章实现了基于MapReduce的手机浏览日志分析
文章简介:主要包含了数据生成部分,数据处理部分,数据存储部分与数据可视化部分
【本文仅供参考!!非唯一答案】其中需求实现的方式有多种,提供的代码并非唯一写法,选择适合的方式即可。

目录

        • 手机日志分析需求
        • 数据生成工具类
        • 模拟数据生成类
        • MapReduce程序需求编写

手机日志分析需求

  • 本文主要实现以下需求
  1. 编写数据生成器生成1G~10G大小的数据,字段必须包括id,日期,手机号码、型号、操作系统字段。
  2. 需要将手机号码4~9为掩码处理。
  3. 分析2021年、2022年操作系统市场占比、手机型号市场占比情况
  4. 分析2022年手机运营商市场占比情况
  5. 分析数据存储到HDFS集群/ana/phone节点下面
  6. 将分析结果存储到Mysql,并进行数据可视化

数据生成工具类

  • 手机号码随机生成
  • 可以采用随机数生成的方式,结合三大运营商的号码前三位数为规则进行生成 代码如下
/**
 * @Description  生成三大运营商的手机号
 */
/**
 * 中国移动手机号段:
 * 134、135、136、137、138、139、147、150、151、152、157、158、159、172、178、182、183、184、187、188、198、1703、1705、1706
 * 中国联通手机号段:
 * 130、131、132、145、155、156、166、171、175、176、185、186、1704、1707、1708、1709
 * 中国电信手机号段:
 * 133、153、173、177、180、181、189、191、193、199、1700、1701、1702
 * 腾讯云API https://market.cloud.tencent.com/products/31101
 */
public class PhoneNumberGenerator {
    //生成一万个手机号码,只需将 generatePhoneNumbers 方法中的参数 count 修改为 10000 即可
    //移动
    private static final String[] CHINA_MOBILE_PREFIX = {"134", "139", "150", "151", "182"};
    //联通
    private static final String[] CHINA_UNICOM_PREFIX = {"130","155","186"};
    //电信
    private static final String[] CHINA_TELECOM_PREFIX = {"133","153","180","181","189"};

    public static void main(String[] args) {
        String phoneNumbers = generatePhoneNumbers(1);
            System.out.println(phoneNumbers);
    }

    public static String generatePhoneNumbers(int count) {
        String phoneNumber=null;
        Random random = new Random();
        for (int i = 0; i < count; i++) {
            String prefix;
            int operatorIndex = random.nextInt(3);
            switch (operatorIndex) {
                case 0:
                    prefix = CHINA_MOBILE_PREFIX[random.nextInt(CHINA_MOBILE_PREFIX.length)];
                    break;
                case 1:
                    prefix = CHINA_UNICOM_PREFIX[random.nextInt(CHINA_UNICOM_PREFIX.length)];
                    break;
                default:
                    prefix = CHINA_TELECOM_PREFIX[random.nextInt(CHINA_TELECOM_PREFIX.length)];
            }
            phoneNumber = prefix + generateRandomNumber(random, 11 - prefix.length());
        }
        return replaceCharacters(phoneNumber,3,8);
    }
    private static String replaceCharacters(String input, int startIndex, int endIndex) {
        if (input == null || input.length() < endIndex) {
            return input;
        }

        StringBuilder sb = new StringBuilder(input);

        for (int i = startIndex; i <= endIndex; i++) {
            sb.setCharAt(i, '*');
        }

        return sb.toString();
    }
    private static String generateRandomNumber(Random random, int length) {
        StringBuilder sb = new StringBuilder();
        for (int i = 0; i < length; i++) {
            sb.append(random.nextInt(10));
        }
        return sb.toString();
    }
}
  • 运营商解析的其中一种方式 【采用接口分析】
  • 这里可以使用鹅厂或者其他厂商开发的接口进行运营商识别 申请获取对应的秘钥即可 例子如下

public class PhoneOperator {
    public static String calcAuthorization(String source, String secretId, String secretKey, String datetime)
            throws NoSuchAlgorithmException, UnsupportedEncodingException, InvalidKeyException {
        String signStr = "x-date: " + datetime + "\n" + "x-source: " + source;
        Mac mac = Mac.getInstance("HmacSHA1");
        Key sKey = new SecretKeySpec(secretKey.getBytes("UTF-8"), mac.getAlgorithm());
        mac.init(sKey);
        byte[] hash = mac.doFinal(signStr.getBytes("UTF-8"));
        String sig = new BASE64Encoder().encode(hash);

        String auth = "hmac id=\"" + secretId + "\", algorithm=\"hmac-sha1\", headers=\"x-date x-source\", signature=\"" + sig + "\"";
        return auth;
    }

    public static String urlencode(Map<?, ?> map) throws UnsupportedEncodingException {
        StringBuilder sb = new StringBuilder();
        for (Map.Entry<?, ?> entry : map.entrySet()) {
            if (sb.length() > 0) {
                sb.append("&");
            }
            sb.append(String.format("%s=%s",
                    URLEncoder.encode(entry.getKey().toString(), "UTF-8"),
                    URLEncoder.encode(entry.getValue().toString(), "UTF-8")
            ));
        }
        return sb.toString();
    }

    public static void main(String[] args) throws NoSuchAlgorithmException, UnsupportedEncodingException, InvalidKeyException {
        //云市场分配的密钥Id
        String secretId = "xx";
        //云市场分配的密钥Key
        String secretKey = "xx;
        String source = "market";

        Calendar cd = Calendar.getInstance();
        SimpleDateFormat sdf = new SimpleDateFormat("EEE, dd MMM yyyy HH:mm:ss 'GMT'", Locale.US);
        sdf.setTimeZone(TimeZone.getTimeZone("GMT"));
        String datetime = sdf.format(cd.getTime());
        // 签名
        String auth = calcAuthorization(source, secretId, secretKey, datetime);
        // 请求方法
        String method = "POST";
        // 请求头
        Map<String, String> headers = new HashMap<String, String>();
        headers.put("X-Source", source);
        headers.put("X-Date", datetime);
        headers.put("Authorization", auth);

        // 查询参数
        Map<String, String> queryParams = new HashMap<String, String>();
        queryParams.put("mobile","XXX");
        // body参数
        Map<String, String> bodyParams = new HashMap<String, String>();
        // url参数拼接
        String url = "https://service-8c43o60c-1253285064.gz.apigw.tencentcs.com/release/sms";
        if (!queryParams.isEmpty()) {
            url += "?" + urlencode(queryParams);
        }

        BufferedReader in = null;
        try {
            URL realUrl = new URL(url);
            HttpURLConnection conn = (HttpURLConnection) realUrl.openConnection();
            conn.setConnectTimeout(5000);
            conn.setReadTimeout(5000);
            conn.setRequestMethod(method);

            // request headers
            for (Map.Entry<String, String> entry : headers.entrySet()) {
                conn.setRequestProperty(entry.getKey(), entry.getValue());
            }

            // request body
            Map<String, Boolean> methods = new HashMap<>();
            methods.put("POST", true);
            methods.put("PUT", true);
            methods.put("PATCH", true);
            Boolean hasBody = methods.get(method);
            if (hasBody != null) {
                conn.setRequestProperty("Content-Type", "application/x-www-form-urlencoded");

                conn.setDoOutput(true);
                DataOutputStream out = new DataOutputStream(conn.getOutputStream());
                out.writeBytes(urlencode(bodyParams));
                out.flush();
                out.close();
            }

            // 定义 BufferedReader输入流来读取URL的响应
            in = new BufferedReader(new InputStreamReader(conn.getInputStream()));
            String line;
            String result = "";
            while ((line = in.readLine()) != null) {
                result += line;
            }
            System.out.println(result);
        } catch (Exception e) {
            System.out.println(e);
            e.printStackTrace();
        } finally {
            try {
                if (in != null) {
                    in.close();
                }
            } catch (Exception e2) {
                e2.printStackTrace();
            }
        }
    }
}

结果如下 (另一种方式为:直接根据前三位手机号进行判断)
在这里插入图片描述

模拟数据生成类

  • 数据生成器 id,日期,手机号码、型号、操作系统

/**
 * @Description
 *  数据生成器  id,日期,手机号码、型号、操作系统
 *  id:UUID 随机生成  日期:2021 2022 手机号码:三大运营商  型号:Apple HuaWei Oppo Vivo Meizu Nokia  操作系统:Apple ios Harmony Samsung
 *  1.分析2021年、2022年操作系统市场占比、手机型号市场占比情况
 *  2.分析2022年手机运营商市场占比情况
 *  3.分析数据存储到HDFS集群/ana/phone节点下面
 *  4.将分析结果存储到Mysql,并进行数据可视化
 */
public class DataGenerator {

    public static void main(String[] args) {
        try {
            BufferedWriter writer = new BufferedWriter(new FileWriter("data/phone.log"));
            for (int i = 0; i < 1000; i++) {
                //UUID随机生成   id,日期,手机号码、型号、操作系统
                String id = UUID.randomUUID().toString();
                String date = getRandomDate();
                String phoneNumber = PhoneNumberGenerator.generatePhoneNumbers(1);
                String model = getRandomModel();
                String operatingSystem = getRandomOperatingSystem();
                String line = id + "," + date + "," + phoneNumber + "," + model + "," + operatingSystem;
                writer.write(line);
                writer.newLine();
            }
            writer.close();
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    private static String getRandomDate() {
        Random random = new Random();
        int year = random.nextInt(2) == 0 ? 2021 : 2022;
        int month = random.nextInt(12) + 1;
        int dayOfMonth;

        if (month == 2) {
            dayOfMonth = random.nextInt(28) + 1;
        } else if (month == 4 || month == 6 || month == 9 || month == 11) {
            dayOfMonth= random.nextInt(30) + 1;
        } else {
            dayOfMonth= random.nextInt(31) + 1;
        }

        return year + "-" +
                (month < 10 ? "0" : "") +
                month+ "-" +
                (dayOfMonth<10? "0":"")+
                dayOfMonth ;
    }

    private static String getRandomPhoneNumber() {
        Random random = new Random();
        StringBuilder phoneNumber = new StringBuilder("1");

        for (int i = 0; i < 10; i++) {
            phoneNumber.append(random.nextInt(10));
        }

        return phoneNumber.toString();
    }

    private static String getRandomModel() {
        String[] models = {"Apple", "HuaWei", "Oppo", "Vivo", "Meizu", "Nokia"};
        return models[new Random().nextInt(models.length)];
    }

    private static String getRandomOperatingSystem() {
        String[] operatingSystems = {"Apple", "HarmonyOS", "Samsung","iOS"};
        return operatingSystems[new Random().nextInt(operatingSystems.length)];
    }
}

结果如下
【Hadoop综合实践】手机卖场大数据综合项目分析_第1张图片

MapReduce程序需求编写

  • 分析2021年、2022年操作系统市场占比、手机型号市场占比情况

/**
 * @Description
 */
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class PhoneOSAnalysis {
    private static int totalCount;
    private static int lineCount2021 = 0;
    private static int lineCount2022 = 0;
    public static class TokenizerMapper extends Mapper<Object, Text, Text, DoubleWritable> {
        private final static DoubleWritable one = new DoubleWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String[] fields = value.toString().split(",");
            if (fields.length >= 5) {
                // 操作系统市场占比
                word.set(fields[1].substring(0, 4) + "-OS-" + fields[4]);
                context.write(word, one);
                // 手机型号市场占比
                word.set(fields[1].substring(0, 4) + "-Model-" + fields[3]);
                context.write(word, one);
            }
        }
    }

    public static class MarketShareReducer extends Reducer<Text, DoubleWritable, Text, DoubleWritable> {
        private DoubleWritable result = new DoubleWritable();

        public void reduce(Text key, Iterable<DoubleWritable> values, Context context)
                throws IOException, InterruptedException {
            double sum = 0;
            for (DoubleWritable val : values) {
                //这里会根据分组的key来计算sum
                sum += val.get();
            }
            int yearTotalCount = key.toString().contains("2021") ? lineCount2021 : lineCount2022;
            double percentage = sum / yearTotalCount;
            result.set(percentage);
            context.write(key, result);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        FileSystem fs = FileSystem.get(conf);
        Path inputPath = new Path("data/phone.log");
        FSDataInputStream inputStream = fs.open(inputPath);
        try (BufferedReader reader = new BufferedReader(new InputStreamReader(fs.open(inputPath)))) {
            String line;
            while ((line = reader.readLine()) != null) {
                if (line.contains("2021")) {
                    lineCount2021++;
                } else if (line.contains("2022")) {
                    lineCount2022++;
                }
            }
        }

//        totalCount = Math.max(lineCount2021, lineCount2022);

        Job job = Job.getInstance(conf, "market share analysis");
        job.setJarByClass(PhoneOSAnalysis.class);
        job.setMapperClass(TokenizerMapper.class);

        // 设置自定义分区器
        job.setPartitionerClass(CustomPartitioner.class);
        job.setNumReduceTasks(2);

        job.setReducerClass(MarketShareReducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);
        Path outputPath = new Path("data/result");
        if (fs.exists(outputPath)) {
            fs.delete(outputPath, true);
        }
        FileInputFormat.addInputPath(job, new Path("data/phone.log"));
        FileOutputFormat.setOutputPath(job, new Path(String.valueOf(outputPath)));
        //        TextInputFormat.addInputPath(job, new Path("hdfs://192.168.192.100:8020/"));
//        TextInputFormat.outInputPath(job, new Path("hdfs://192.168.192.100:8020/"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

    public static class CustomPartitioner extends Partitioner<Text, DoubleWritable> {

        @Override
        public int getPartition(Text key, DoubleWritable value, int numPartitions) {
            // 根据年份进行分区
            if (key.toString().contains("2021")) {
                return 0;
            } else {
                return 1;
            }
        }
    }
}
  • 分析2022年手机运营商市场占比情况

/**
 * @Description
 */
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class OperatorMR {
    private static int lineCount2022 = 0;

    public static class TokenizerMapper extends Mapper<Object, Text, Text, DoubleWritable> {
        private final static DoubleWritable one = new DoubleWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String[] fields = value.toString().split(",");
            if (fields.length >= 3 && fields[1].contains("2022")) {
                // 手机运营商市场占比
                word.set(fields[1].substring(0, 4) + "-Operator-" + getCarrier(fields[2]));
                context.write(word, one);
            }
        }

        private String getCarrier(String phoneNumber) {
            String prefix = phoneNumber.substring(0, 3);
            switch (prefix) {
                //"133","153","180","181","189"
                case "133":
                case "153":
                case "180":
                case "181":
                case "189":
                    return "电信";
                //"130","155","186"
                case "130":
                case "155":
                case "186":
                    return "联通";
                default:
                    return "移动";
            }
        }
    }

    public static class MarketShareReducer extends Reducer<Text, DoubleWritable, Text, DoubleWritable> {
        private DoubleWritable result = new DoubleWritable();

        public void reduce(Text key, Iterable<DoubleWritable> values, Context context)
                throws IOException, InterruptedException {
            double sum = 0;
            for (DoubleWritable val : values) {
                sum += val.get();
            }
            double percentage = sum / lineCount2022;
            result.set(percentage);
            context.write(key, result);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        FileSystem fs = FileSystem.get(conf);
        Path inputPath = new Path("data/phone.log");

        try (BufferedReader reader = new BufferedReader(new InputStreamReader(fs.open(inputPath)))) {
            String line;
            while ((line = reader.readLine()) != null) {
                if (line.contains("2022")) {
                    lineCount2022++;
                }
            }
        }

        Job job = Job.getInstance(conf, "PhoneOperator");
        job.setJarByClass(OperatorMR.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setReducerClass(MarketShareReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);

        Path outputPath = new Path("data/result-phone");

        if (fs.exists(outputPath)) {
            fs.delete(outputPath, true);
        }
//        TextInputFormat.addInputPath(job, new Path("hdfs://192.168.192.100:8020/"));
//        TextInputFormat.outInputPath(job, new Path("hdfs://192.168.192.100:8020/"));
        FileInputFormat.addInputPath(job, new Path("data/phone.log"));
        FileOutputFormat.setOutputPath(job, outputPath);
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

结果如下
【Hadoop综合实践】手机卖场大数据综合项目分析_第2张图片
【Hadoop综合实践】手机卖场大数据综合项目分析_第3张图片
【Hadoop综合实践】手机卖场大数据综合项目分析_第4张图片

-将分析结果存储到Mysql,并进行数据可视化

package com.yopai.mrmysql;

/**
 * @Description
 */

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;


public class OPMysqlMR {
    private static int lineCount2022 = 0;

    public static class TokenizerMapper extends Mapper<Object, Text, Text, DoubleWritable> {
        private final static DoubleWritable one = new DoubleWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String[] fields = value.toString().split(",");
            if (fields.length >= 3 && fields[1].contains("2022")) {
                // 手机运营商市场占比
                word.set(fields[1].substring(0, 4) + "-Operator-" + getCarrier(fields[2]));
                context.write(word, one);
            }
        }

        private String getCarrier(String phoneNumber) {
            String prefix = phoneNumber.substring(0, 3);
            switch (prefix) {
                case "133":
                case "153":
                case "180":
                case "181":
                case "189":
                    return "电信";
                case "130":
                case "155":
                case "186":
                    return "联通";
                default:
                    return "移动";
            }
        }
    }

    public static class MarketShareReducer extends Reducer<Text, DoubleWritable, DBOutputWritable, NullWritable> {
        private DoubleWritable result = new DoubleWritable();

        public void reduce(Text key, Iterable<DoubleWritable> values, Context context)
                throws IOException, InterruptedException {
            double sum = 0;
            for (DoubleWritable val : values) {
                sum += val.get();
            }
            double percentage = sum / lineCount2022;
            result.set(percentage);
            context.write(new DBOutputWritable(key.toString(), result.get()), NullWritable.get());
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();

        // 设置数据库连接信息
        String dbUrl = "jdbc:mysql://localhost:3306/blog";
        String dbUsername = "root";
        String dbPassword = "Admin2022!";

        DBConfiguration.configureDB(conf, "com.mysql.jdbc.Driver", dbUrl, dbUsername, dbPassword);

        try (Connection connection = DriverManager.getConnection(dbUrl, dbUsername, dbPassword)) {
            String createTableSql = "CREATE TABLE IF NOT EXISTS operator_market_share(operator VARCHAR(255), market_share DOUBLE)";
            PreparedStatement preparedStatement = connection.prepareStatement(createTableSql);
            preparedStatement.executeUpdate();
        }

        FileSystem fs = FileSystem.get(conf);
        Path inputPath = new Path("data/phone.log");

        try (BufferedReader reader = new BufferedReader(new InputStreamReader(fs.open(inputPath)))) {
            String line;
            while ((line = reader.readLine()) != null) {
                if (line.contains("2022")) {
                    lineCount2022++;
                }
            }
        }

        Job job = Job.getInstance(conf, "PhoneOperator");
        job.setJarByClass(OPMysqlMR.class);

        job.setMapperClass(TokenizerMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(DoubleWritable.class);

        job.setReducerClass(MarketShareReducer.class);
        job.setOutputKeyClass(DBOutputWritable.class);
        job.setOutputValueClass(NullWritable.class);

        // 设置数据库输出
        DBOutputFormat.setOutput(job, "operator_market_share", "operator", "market_share");

        FileInputFormat.addInputPath(job, new Path("data/phone.log"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}
package com.yopai.mrmysql;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.lib.db.DBWritable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;

/**
 * @Description
 */
public class DBOutputWritable implements Writable, DBWritable {
    private String operator;
    private double market_share;

    public DBOutputWritable() {
    }

    public DBOutputWritable(String operator, double market_share) {
        this.operator = operator;
        this.market_share = market_share;
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        operator = in.readUTF();
        market_share = in.readDouble();
    }

    @Override
    public void write(DataOutput out) throws IOException, IOException {
        out.writeUTF(operator);
        out.writeDouble(market_share);
    }

    @Override
    public void readFields(ResultSet resultSet) throws SQLException {
        // 不需要实现此方法,因为我们只会写入数据到数据库
    }

    @Override
    public void write(PreparedStatement preparedStatement) throws SQLException {
        preparedStatement.setString(1, operator);
        preparedStatement.setDouble(2, market_share);
    }
}

运行结果如下
【Hadoop综合实践】手机卖场大数据综合项目分析_第5张图片

  • 可视化操作
package com.yopai.draw;

/**
 * @Description
 */
import java.awt.*;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.Statement;
import javax.swing.JFrame;
import org.jfree.chart.ChartFactory;
import org.jfree.chart.ChartPanel;
import org.jfree.chart.JFreeChart;
import org.jfree.chart.plot.PiePlot;
import org.jfree.data.general.DefaultPieDataset;

public class PieChartExample extends JFrame {

    public PieChartExample() {
        // 从数据库获取数据
        DefaultPieDataset dataset = new DefaultPieDataset();
        try {
            String dbUrl = "jdbc:mysql://localhost:3306/blog";
            String dbUsername = "root";
            String dbPassword = "Admin2022!";
            Connection connection = DriverManager.getConnection(dbUrl, dbUsername, dbPassword);
            Statement statement = connection.createStatement();
            ResultSet resultSet = statement.executeQuery("SELECT operator, market_share FROM operator_market_share");
            while (resultSet.next()) {
                String operator = resultSet.getString("operator");
                double marketShare = resultSet.getDouble("market_share");
                dataset.setValue(operator, marketShare);
            }
        } catch (Exception e) {
            e.printStackTrace();
        }

        // 创建饼图
        JFreeChart pieChart = ChartFactory.createPieChart(
                "运营商市场占比",   // 图表标题
                dataset,          // 数据集
                true,             // 是否显示图例
                true,             // 是否生成工具提示
                false             // 是否生成URL链接
        );
        // 设置字体以显示中文
        Font font = new Font("宋体", Font.PLAIN, 12);
        pieChart.getTitle().setFont(font);
        pieChart.getLegend().setItemFont(font);
        PiePlot plot = (PiePlot) pieChart.getPlot();
        plot.setLabelFont(font);
        // 添加饼图到面板并显示
        ChartPanel chartPanel = new ChartPanel(pieChart);
        setContentPane(chartPanel);
    }

    public static void main(String[] args) {
        PieChartExample pieChartExample = new PieChartExample();
        pieChartExample.setSize(600, 600);
        pieChartExample.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
        pieChartExample.setVisible(true);
    }
}

结果如下
【Hadoop综合实践】手机卖场大数据综合项目分析_第6张图片
本篇文章到这里结束 需要注意的是每个人的环境不用调用的API会有所差异。

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